As no optical imaging system can ever be perfect, improvements are desirable that can correct or minimize the effect of imperfections on the resulting images. One way to improve the images produced by an optical system is to characterize the optical flaws and artifacts and correct the resulting images based on this information. One way to do this is image normalization. Image normalization is a process whereby a reference image is made of what is usually a uniform surface or other subject suitable as a reference. The reference image is made under conditions that exhibits any optical artifacts of the system. For example, a black and white CCD camera may have individual or groups of inoperative/defective pixels. These would show up as black or white spots in a reference image taken of a uniform surface. Because these artifacts are constant, they will appear in any image taken with that particular device. A normalized image can then be created by using the collected reference image, which, when properly combined, “normalizes” the flawed pixels so that they appear to have the same value as all of the remaining pixels of the image of the uniformly illuminated subject. Obviously, this technique cannot reproduce the value the flawed pixel should have been had it operated properly. However, the overall effect of the flawed pixel on the image quality is, on average, reduced, if the value is normalized instead of being left as full black or white. For example, if pixels can have values of 1 to 100, and a flawed pixel is normalized to 50, the most the pixel can be off from the true value is 50, whereas if the pixel is stuck at 1 or 100, the pixel could be off as far as 99. One skilled in the art will recognize that more sophisticated normalization methods exist for correcting images as light values are not linear, and, for example, artificial intelligence means can be employed to “normalize” known flawed areas to the values of surrounding areas, instead of to a fixed value.
In the field of electron microscopy, examples of “fixed flaws” include, detector irregularities (pixel non-uniformity and fixed pattern contrast from the scintillator), dust and scratches on the scintillator surface. These flaws produce artifacts in the raw unprocessed image that can be manifested as dark shadows, excessively bright highlights, specks and mottles that alter the true pixel value data.
Normalization of images to correct for optical artifacts requires that the flaws and their effect on the image is fixed. If the reference image appears different each time it is taken, it is not possible to prepare a corrective image to normalize subsequent images.
One optical artifact that cannot be normalized with a single reference image is that of unequal illumination to the CCD sensor. For example, in a Transmission Electron Microscope (TEM), depending on where the imaging device is physically placed on the microscope's column, illumination of the device can be markedly brighter at the center than at the edges. If this effect is constant for all images, then it can be normalized. However, where certain operating parameters (accelerating voltage, magnification, probe size, beam intensity, etc.) of the TEM are adjustable, the effect of unequal illumination is not constant for all microscope settings. Thus, while a normalization image may be able to correct for fixed flaws, such as bad pixels, a single reference image is incapable of compensating for the effect of unequal illumination where that effect is not constant at different microscope settings (e.g., magnification or accelerating voltage). Thus, a need exists for an image normalization method that can normalize optical artifacts in a system where the artifacts vary depending on microscope parameters or other system settings.